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Carolina Institute for Battery Innovation (CIBI). Specific research areas of interest for this search include: i) electrochemical modeling, preferably including advanced machine learning and AI; ii) battery
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to candidates with teaching or research interests in: Algorithms Science of Programming Computer Vision Machine Learning Qualifications Applicants must have: A Ph.D. in Computer Science, Data Science, Artificial
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members to conduct novel research. Key Responsibilities Produce novel computational models of biological systems (e.g. MuJoCo, MJX, Blender, aerodynamic modeling approaches) 30% Apply machine learning and
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• At least a bachelor’s degree in civil engineering, hydrology, water resources engineering, optimization, seasonal forecasting, machine learning, or from similar programs. • Master’s degree
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University of Chicago, and part of the Data Science and Learning Division at Argonne National Labs, develop and deploy cutting edge technologies to solve new challenges facing the scientific community and
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research in formal verification, machine learning and artificial intelligence system assurance. The successful candidate will develop new techniques and tools for analysing, verifying and improving
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25.08.2026 Application deadline : 20.09.2026 The Cluster of Excellence "Machine Learning - New Perspectives for Science" together with the Tübingen AI Center at the University of Tübingen offers a
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Texas A&M Engineering Extension Service TEEX | Brownsville, Texas | United States | about 6 hours ago
of adult learning theories and learning styles to present and maximize learner success to a broad audience in a manner to ensure effectiveness of course instruction. This position reports to the ITSI
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Texas A&M Engineering Extension Service TEEX | Brownsville, Texas | United States | about 6 hours ago
to online learning platforms. Educational reimbursement assistance and release time. Leadership development programs TEEX Employer Notices Military Crosswalk Info Applications must either have
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approaches, or critical mineral and rare-earth element recovery to address pressing national and global supply chain challenges. Applicants with experience applying machine learning tools in their research